Method and device for predicting service life of lithium iron phosphate battery and storage medium

Abstract

The invention relates to the field of batteries, and discloses a method and device for predicting the service life of a lithium iron phosphate battery and a storage medium, and the method comprises the steps: obtaining the cycle curves of a plurality of batteries and the corresponding total cycle number N, and obtaining the DC internal resistance change slope R through the cycle curve of each battery, and a prediction function for predicting the service life of the lithium iron phosphate battery is further obtained through the total cycle number N of all the batteries and the corresponding direct-current internal resistance change slope R. According to the method provided by the invention, tedious calculation or long-time cycle is not needed, and the cycle life of the lithium iron phosphate battery to be predicted is predicted in a simple and efficient manner. Linear fitting is carried out on DCIR line segments in the early cycle period to calculate the slope, and therefore the service life of the lithium iron phosphate battery in the later cycle period is rapidly predicted.

Description

Technical Field

[0001] The present invention relates to the field of batteries, and in particular to a method, device, and storage medium for predicting the lifespan of lithium iron phosphate batteries. Background Art

[0002] In recent years, with the application of new battery technologies and materials, the performance of lithium iron phosphate batteries has been greatly improved. Lithium iron phosphate batteries, which combine low cost, high safety, high energy density, and excellent high-temperature performance, are increasingly favored by the industry. Their market share has been continuously expanding, and they are widely used in daily life. Common applications of lithium iron phosphate batteries include electric vehicles, energy storage systems, military equipment, and aerospace operations.

[0003] Compared with lead-acid batteries, it has a good lifespan; compared with nickel-metal hydride batteries, it has a higher working preset voltage; compared with nickel-cadmium batteries, it is more environmentally friendly, which is also an important reason for the lithium iron phosphate to set off a lithium battery storm. Although ternary batteries are popular at present, due to the safety problems that are difficult to control under high energy density in ternary batteries, domestic battery companies are developing lithium iron phosphate batteries towards large-scale.

[0004] The performance degradation of lithium iron phosphate batteries during use is a key technical difficulty, which restricts the remaining service life of the batteries. As the potential of lithium iron phosphate batteries is gradually developed to the limit, an equally important problem has emerged, that is, how to efficiently predict their lifespan to extend the service life. Accurately predicting the remaining service life of lithium iron phosphate batteries can not only ensure the safe and reliable operation of the system, but also maximize the utilization of the remaining value of the batteries. Therefore, the prediction of the remaining life is crucial for battery management and cascaded utilization, and the research in this direction has become a current research hotspot.

[0005] CN117214761A discloses a method for qualitatively predicting the cycle life of energy storage lithium iron phosphate batteries. This method proposes a method for predicting the cycle life of lithium iron phosphate batteries based on the degree of graphitization phase change (G) of the negative electrode and the electrochemical parameter ΔPeakS. This method innovatively combines material phase change and electrochemical data, simplifies the test process, and improves the prediction reliability. However, this method is still limited to the prediction within a small range of capacity loss.

[0006] CN110555226A discloses a method for predicting the remaining life of a lithium iron phosphate battery based on empirical mode decomposition (EMD) and a multi-layer perceptron (MLP). This method decomposes the battery capacity data into multi-scale intrinsic mode functions through EMD, and then inputs the decomposed features into the MLP model for training and prediction, and finally verifies the model accuracy. This method combines signal decomposition and deep learning, improving the prediction efficiency and accuracy. Experiments show that the root mean square error is as low as 0.0029. However, this method only sets 2 neurons in the hidden layer of the MLP and only shows short-term prediction results, having the defects of being difficult to capture complex aging patterns and the reliability of long-term life prediction being unknown. Summary of the Invention

[0007] The object of the present invention is to overcome the problems of inaccurate prediction of the cycle life of lithium iron phosphate batteries and limited application scope existing in the prior art, and provide a method, device and storage medium for predicting the life of lithium iron phosphate batteries. This method achieves the purpose of quickly predicting the life of lithium iron phosphate batteries in the later stage of cycling through a simple and efficient way without cumbersome calculations or long-term cycling.

[0008] To achieve the above object, in the first aspect of the present invention, a method for predicting the life of a lithium iron phosphate battery is provided, wherein the method includes:

[0009] Obtain the cycle curves of multiple batteries and the total number of cycles N corresponding thereto, wherein the cycle curve is the curve when the battery is cycled to a preset value of the rated capacity at a preset temperature and a preset voltage, and the preset temperatures of each battery are the same, and their preset pressures are different;

[0010] Based on the cycle curves of each battery, obtain the corresponding direct current internal resistance according to a preset number of cycle weeks;

[0011] Based on the preset number of cycle weeks of each battery and the corresponding direct current internal resistance thereof, perform linear fitting respectively to obtain the direct current internal resistance change slope R of each battery;

[0012] Based on the total number of cycles N of all the batteries and the corresponding direct current internal resistance change slope R thereof, perform linear fitting to obtain a prediction function for predicting the life of the lithium iron phosphate battery;

[0013] Based on the prediction function, determine the cycle life of the lithium iron phosphate battery to be predicted.

[0014] The research found that the aging failure modes of the anode of lithium iron phosphate batteries are the aging and thickening of the solid electrolyte interface film (SEI film) and the degree of graphitization. The corrosion of the positive electrode electrolyte and the destruction of the crystal structure have a much smaller impact on the capacity attenuation than the growth of the SEI film on the negative electrode. It can be considered that the capacity attenuation and the increase in the direct current internal resistance (DCIR) are mainly due to the thickening of the SEI. During this period, the electrolyte continues to react with graphite, the SEI continues to age and thicken, and the DCIR is almost linear with respect to the number of cycles. Therefore, the battery life can be predicted by linearly fitting the DCIR and according to its change slope.

[0015] Based on the above principles, the present invention provides a method for quickly and accurately predicting the life of lithium iron phosphate batteries. The method provided by the present invention does not require cumbersome calculations or long-term cycling. Through a simple and efficient method, it realizes the linear fitting calculation of the slope of the DCIR line segment in the early stage of cycling, and then quickly predicts the life of lithium iron phosphate batteries in the later stage of cycling.

[0016] In the art, the cycle life of a battery is generally defined as the number of cycles when the battery capacity decays to 80% of the rated capacity. Therefore, in this application, it is cycled to a preset value of the rated capacity, and this preset value is generally 80% of the rated capacity. The charge and discharge current when obtaining the cycle curve is the nominal current of the battery.

[0017] The preset number of cycles can be set according to the same or different gradients. For example, the preset number of cycles can be an arithmetic sequence or increase according to a certain rule.

[0018] Preferably, the cycle curves of the multiple batteries include the curves of at least one of the batteries when cycled to the preset value of the rated capacity at a preset voltage less than 1V, and the curves of at least one of the batteries when cycled to the preset value of the rated capacity at a preset voltage not less than 1V. Since when the voltage is less than 1V, such as 0.5V, severe lithium deposition occurs on the negative electrode of the battery, generally, the data obtained by cycling at a preset voltage lower than 1V is not included in the analysis scope when predicting the battery life. Since the present invention uses the key parameter of the change slope R of the direct current internal resistance to predict the battery life, which has good stability, the present invention can break through and include the cycle curves of the cycling test at a preset voltage lower than 1V in the analysis scope, thereby further improving the accuracy of the prediction. The preset voltage less than 1V can be 0.5V, 0.7V, etc., and the preset voltage not less than 1V can be 1V, 1.5V, 2V, 2.5V, etc.

[0019] Preferably, the preset temperature is 20 - 65°C. The measurement temperature of the cycling curve not only affects the prediction accuracy of the finally obtained prediction function, but also affects the time required for the entire prediction. The measurement temperature can be 20°C, 25°C, 30°C, 40°C, 45°C, 50°C, 60°C, 65°C, and any value between any two of them.

[0020] Preferably, the preset temperature is 40°C - 45°C. At this temperature, not only can the prediction accuracy be further improved, but also the time required for prediction can be effectively shortened.

[0021] Preferably, the preset number of cycling weeks does not exceed 300 cycles.

[0022] Preferably, it is determined that the preset voltage is less than 1V, and the preset number of cycling weeks includes at least one of the 50th - 100th cycles;

[0023] It is determined that the preset voltage is not less than 1V, and the preset number of cycling weeks is not less than the 100th cycle.

[0024] During the cycles before 50 weeks, the DC internal resistance shows a downward trend. After 50 weeks, the DC internal resistance shows an upward trend. And the battery is in the activation stage before 100 weeks. Therefore, in the existing prediction methods, the cycling curves before 100 weeks are generally not included in the analysis scope. Since the present invention uses the key parameter of the change slope R of the DC internal resistance to predict the battery life, it is not easily affected by a single cycling curve. Therefore, the present invention has breakthroughly included the cycling curves of the 50th - 100th weeks in the analysis scope, not only effectively improving the prediction accuracy, but also greatly shortening the prediction time and improving the prediction efficiency.

[0025] Preferably, the step of determining the cycling life of the lithium iron phosphate battery to be predicted based on the prediction function includes:

[0026] Obtain the change slope R of the DC internal resistance of the lithium iron phosphate battery to be predicted at the measured time voltage and the preset temperature 待测 ;

[0027] Substitute the R 待测 into the prediction function to obtain the cycling life of the lithium iron phosphate battery to be predicted.

[0028] The preset temperature is the same as the preset temperature when obtaining the cycling curves of the multiple batteries. The test voltage is selected as a suitable voltage from the preset voltages when obtaining the cycling curves of the multiple batteries. This test voltage is generally not less than 1V.

[0029] When measuring the cycling life of the lithium iron phosphate battery to be predicted, first, it is necessary to obtain the change slope R of its DC internal resistance 待测, the steps for obtaining it are the same as those for obtaining the DC internal resistance change slope R of each of the batteries. Specifically: perform charge and discharge cycles at the test voltage and the preset temperature, and cycle until the preset number of cycles, where the preset number of cycles does not exceed 300 cycles. Then extract the DC internal resistance in the corresponding cycle curve according to the preset number of cycle weeks, and perform linear fitting based on the number of cycle weeks and the corresponding DC internal resistance to obtain the R of the lithium iron phosphate battery to be predicted. 待测 .

[0030] Preferably, the number of the preset number of cycle weeks is not less than 3, and they form an arithmetic sequence. It is possible to confirm whether the change in the DC internal resistance is linear through no less than 3 data. As long as it shows a linear change, linear fitting can be performed to obtain its change rate. All the preset numbers of cycle weeks form an arithmetic sequence.

[0031] Preferably, it is determined that the preset voltage is less than 1V, and the number of the preset number of cycle weeks is 3. When the preset voltage is less than 1V, the battery is in an over-discharge cycle and decays very fast. It is necessary to reduce the extraction quantity and extract 3 cycle weeks for analysis, which can not only reduce the data processing volume but also accurately know whether the change in its DC internal resistance is linear.

[0032] Preferably, it is determined that the preset voltage is not less than 1V, and the number of the preset number of cycle weeks is 4.

[0033] The second aspect of the present invention provides a device for predicting the life of a lithium iron phosphate battery. Among them, the device includes:

[0034] The first acquisition module is used to acquire the cycle curves of multiple batteries and the total number of cycle turns N corresponding thereto. Among them, the cycle curve is the curve when the battery cycles to a preset value of the rated capacity at the preset temperature and the preset voltage, and the preset temperatures of each of the batteries are the same, and their preset pressures are different;

[0035] The second acquisition module is used to acquire the DC internal resistance corresponding to each of the batteries according to the preset number of cycle weeks based on the cycle curves of each of the batteries;

[0036] The first fitting module is used to perform linear fitting respectively based on the preset number of cycle weeks of each of the batteries and the corresponding DC internal resistance thereof to obtain the DC internal resistance change slope R of each of the batteries;

[0037] The second fitting module is used to perform linear fitting based on the total number of cycle turns N of all the batteries and the corresponding DC internal resistance change slope R to obtain a prediction function for predicting the life of the lithium iron phosphate battery;

[0038] The prediction module is used to determine the cycle life of the lithium iron phosphate battery to be predicted based on the prediction function.

[0039] In the third aspect of the present invention, a storage medium for predicting the life of a lithium iron phosphate battery is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method described in the first aspect of the present invention are implemented.

[0040] The method provided by the present invention does not require cumbersome calculations or long-term loops. Through a simple and efficient manner, it realizes the linear fitting calculation of the slope of the DCIR line segment in the early stage of the cycle, thereby quickly predicting the life of the lithium iron phosphate battery in the later stage of the cycle. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 is a schematic flowchart of a method for predicting the life of a lithium iron phosphate battery in an embodiment;

[0042] Figure 2 is a dotted line graph of the DC internal resistance and the corresponding number of cycle weeks of each battery obtained in a specific embodiment;

[0043] Figure 3 is a dotted line graph of the slope R of the DC internal resistance change and the corresponding total number of cycle turns N obtained in a specific embodiment;

[0044] Figure 4 is a schematic diagram of a device for predicting the life of a lithium iron phosphate battery provided in an embodiment. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0045] In the ranges disclosed herein, the endpoints and any values are not limited to the exact ranges or values. These ranges or values should be understood to include values close to these ranges or values. For numerical ranges, the endpoint values of each range, between the endpoint values of each range and individual point values, and between individual point values can be combined with each other to obtain one or more new numerical ranges, and these numerical ranges should be regarded as specifically disclosed herein.

[0046] In the description of this specification, the description with reference to terms such as "an embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0047] The endpoints and any values within the ranges disclosed in this document are not limited to the exact ranges or values. These ranges or values should be understood to include values close to these ranges or values. For numerical ranges, the values between the endpoint values of each range, between the endpoint values of each range and individual point values, and between individual point values can be combined with each other to obtain one or more new numerical ranges, and these numerical ranges should be regarded as specifically disclosed in this document.

[0048] In addition, the term "and / or" in the specification and claims is used to describe the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone. The character " / " generally indicates that the associated objects before and after are in an "or" relationship.

[0049] Accurately predicting the remaining service life of lithium iron phosphate batteries can not only ensure the safe and reliable operation of the system, but also maximize the utilization of the remaining value of the batteries. Since the existing prediction methods are either cumbersome to operate or inaccurate in prediction, the present invention proposes a method for simply, quickly, and accurately predicting the life of lithium iron phosphate batteries.

[0050] In one embodiment, as Figure 1 shown, a method for predicting the life of a lithium iron phosphate battery is provided, and the method includes:

[0051] S110, obtaining the cycle curves of multiple batteries and their corresponding total number of cycle turns N, where the cycle curve is the curve when the battery is cycled to a preset value of the rated capacity at a preset temperature and a preset voltage, and the preset temperatures of each battery are the same, and their preset pressures are different;

[0052] S120, based on the cycle curves of each battery, obtaining the corresponding direct current internal resistance according to a preset number of cycle weeks;

[0053] S130, respectively performing linear fitting on the preset number of cycle weeks of each battery and its corresponding direct current internal resistance to obtain the change slope R of the direct current internal resistance of each battery;

[0054] S140, performing linear fitting based on the total number of cycle turns N of all the batteries and their corresponding change slopes R of the direct current internal resistance to obtain a prediction function for predicting the life of the lithium iron phosphate battery;

[0055] S150, determining the cycle life of the lithium iron phosphate battery to be predicted based on the prediction function.

[0056] When cycling to a preset value of the rated capacity generally means cycling to 80% of the rated capacity.

[0057] In this embodiment, based on the cycle curves of each of the batteries, obtaining the corresponding DC internal resistance according to a preset number of cycles specifically refers to: according to a preset number of cycles, such as the 50th cycle, the 100th cycle, the 150th cycle, the 200th cycle, the 250th cycle, etc., extracting the DC internal resistance (DCIR) line segment from the open circuit voltage (OCV) of the cycle curve of the battery at the preset number of cycles, and obtaining the DC internal resistance. The specific steps include:

[0058] Extract the DCIR line segment in the open circuit voltage (OCV) of the preset number of cycles for analysis, obtain the voltage V1 at the moment when the battery applies the load and the voltage V2 after stabilization, as well as the applied current value I (generally the nominal current of the battery), and then calculate according to the following formula:

[0059] DCIR = (V1 - V2) / I, where V1 is the open circuit voltage before applying the load, V2 is the stabilized voltage after applying the load, and I is the applied current.

[0060] A cycling test can be carried out manually or by an instrument, and the cycle curves of the multiple batteries and the data of the corresponding total number of cycles N are stored in a database for future use.

[0061] The preset number of cycles is extracted according to the preset n1th cycle, n2th cycle, n3th cycle, n4th cycle, n5th cycle... etc., and n1, n2, n3, n4, n5... etc. are natural numbers.

[0062] In one embodiment, the cycle curves of the multiple batteries include the curve of at least one of the batteries when cycling to a preset value of the rated capacity at a preset voltage less than 1V, and the curve of at least one of the batteries when cycling to a preset value of the rated capacity at a preset voltage not less than 1V. Generally, the cycle curve includes a cycle curve at a preset voltage less than 1V, and two cycle curves at preset voltages not less than 1V.

[0063] In one embodiment, the preset temperature is 20 - 65°C. The preset temperature can be 20°C, 30°C, 40°C, 45°C, 50°C, 60°C, 65°C, etc.

[0064] In one embodiment, the preset temperature is 40°C - 45°C.

[0065] In one embodiment, the preset number of cycles does not exceed 300 cycles. The preset number of cycles can be multiple numbers increasing in an arithmetic progression such as the 50th cycle, the 100th cycle, the 150th cycle, the 200th cycle, the 250th cycle, the 300th cycle, etc.

[0066] In one embodiment, it is determined that the preset voltage is less than 1V, and the preset number of cycles includes at least one of the 50th to 100th cycles. It is determined that the preset voltage is not less than 1V, and the preset number of cycles is not less than the 100th cycle.

[0067] In one embodiment, the step of determining the cycle life of the lithium iron phosphate battery to be predicted based on the prediction function includes:

[0068] Obtain the slope R of the change in the direct current internal resistance of the lithium iron phosphate battery to be predicted at the test voltage and the preset temperature 待测 ;

[0069] Substitute the R 待测 into the prediction function to obtain the cycle life of the lithium iron phosphate battery to be predicted.

[0070] Once the preset temperature for measuring the cycle curves of multiple batteries is determined, then the preset temperature when obtaining R 待测 is the same as it, and the test voltage when obtaining R 待测 is one of the preset voltages at which multiple batteries are cycled at different preset voltages. Generally, it is sufficient that the preset voltage when measuring R 待测 is not less than 1V.

[0071] The step of obtaining R 待测 is the same as the step of obtaining the slope R of the direct current internal resistance change of each battery, specifically as follows: Perform charge and discharge cycling at the test voltage and the preset temperature, and cycle to the preset number of cycles, where the preset number of cycles does not exceed 300 cycles. Then, extract the direct current internal resistance in the corresponding cycle curve according to the preset number of cycles, and perform linear fitting based on the number of cycles and the corresponding direct current internal resistance to obtain R to be measured.

[0072] In one embodiment, it is determined that the preset voltage is less than 1V, and the number of the preset number of cycles is 3.

[0073] In one embodiment, it is determined that the preset voltage is not less than 1V, and the number of the preset number of cycles is 4.

[0074] In a specific embodiment, a method for predicting the life of a lithium iron phosphate battery is provided, including:

[0075] S210, at 45°C, three lithium iron phosphate batteries of the same batch after formation (formation at 0.1C) and grading (specific parameters: soft package laminated, 110 mAh; positive active material LiFePO4, nominal specific capacity 149.4 mAh·g -1 , proportion 96.9%; negative active material Graphite, nominal specific capacity 344 mAh·g -1, with a proportion of 95.55%; N / P 1.09) were aged cyclically under three conditions of 0.5V, 1.5V, and 2.5V, and the current was 1C (1C = 150 mAg -1 ) cycled until the capacity reached 80% of the rated capacity, obtaining the cycle curves and total number of cycle turns of the three batteries at the corresponding preset voltages respectively. The total number of cycle turns of the three batteries were 282, 1200, and 1684 turns respectively (to further improve the accuracy of the data, multiple parallel tests can also be carried out at the same preset voltage, which is easy to implement in this field and will not be elaborated here).

[0076] S220. Extract DCIR from the OCV in the cycle curves of the three batteries. The specific steps include:

[0077] S2201. Extract the DCIR of the 52nd turn, 103rd turn, and 154th turn of the battery cycled at 0.5V, which are 452.27 mΩ, 458.42 mΩ, and 476.86 mΩ respectively;

[0078] S2202. Extract the DCIR of the 103rd turn, 154th turn, 205th turn, and 256th turn of the battery cycled at 1.5V, which are 431.99 mΩ, 430.3 mΩ, 432.18 mΩ, and 446.16 mΩ respectively;

[0079] S2203. Extract the DCIR of the 103rd turn, 154th turn, 205th turn, and 256th turn of the battery cycled at 2.5V, which are 441.69 mΩ, 444.85 mΩ, 445.15 mΩ, and 447.5 mΩ respectively;

[0080] S230. Plot the DCIR of the three batteries at their corresponding preset voltages and their corresponding number of cycle turns as a dot-line graph as shown in Figure 2 shown, and fit the dot-lines of the DCIR and their corresponding number of cycle turns of each battery into a straight line to obtain the slope R of the change in the direct current internal resistance of each battery.

[0081] In this embodiment, the slopes R of the DCIR changes of the three batteries cycled at 0.5V, 1.5V, and 2.5V are 0.24108, 0.08704, and 0.03476 respectively.

[0082] S240. Take the slope R of the change in the direct current internal resistance of the three batteries as the abscissa x, and the corresponding total number of cycle turns N as the ordinate y, plot a dot-line graph, and fit it into a straight line as shown in Figure 3 shown, obtaining the prediction function of the battery life: y = -6605x + 1756, where y is the predicted cycle life (turns), and x is the slope of the change in the direct current internal resistance of the battery to be predicted.

[0083] In this prediction function, the cycle life of the lithium iron phosphate battery to be predicted has a negative correlation with the slope of the change in its DC internal resistance, that is, the greater the slope of the change in DC internal resistance, the smaller its cycle life.

[0084] S250. Based on the prediction function, determine the cycle life of the lithium iron phosphate battery to be predicted.

[0085] In this example, the lithium iron phosphate battery to be predicted is aged cyclically under the conditions of a preset temperature of 45°C and a test voltage of 1V, and the DCIRs at the 103rd, 154th, 205th, and 256th cycles are extracted, which are 465.65 mΩ, 467.67 mΩ, 475.61 mΩ, and 482.67 mΩ respectively.

[0086] Taking the DCIR of the lithium iron phosphate battery to be predicted as the ordinate and the corresponding number of cycles as the abscissa, draw a dot-line graph and perform linear fitting to obtain the slope R of the change in its DC internal resistance. 待测 is 0.11569. Substitute x = 0.11569 into the prediction function y = -6605x + 1756, and it is obtained that when the discharge capacity cycles to 80% of the rated capacity, the number of cycles is approximately 991.

[0087] Continue to cycle the lithium iron phosphate battery to be predicted and observe its discharge capacity. Finally, when the discharge capacity reaches 80% of the rated capacity, the total number of cycles obtained is 969. The actual cycle life is not much different from the predicted life, which proves the effectiveness of this method.

[0088] In another embodiment of the present invention, as Figure 4 shown, there is also provided a device for predicting the life of a lithium iron phosphate battery, and the device includes:

[0089] The first acquisition module 401 is used to acquire the cycle curves of multiple batteries and their corresponding total number of cycles N. Among them, the cycle curve is the curve when the battery cycles to a preset value of the rated capacity under a preset temperature and a preset voltage, and the preset temperatures of each battery are the same, and their preset pressures are different;

[0090] The second acquisition module 402 is used to obtain the DC internal resistance corresponding to each battery according to a preset number of cycle weeks based on the cycle curves of each battery;

[0091] The first fitting module 403 is used to perform linear fitting on the preset number of cycle weeks of each battery and its corresponding DC internal resistance respectively to obtain the slope R of the change in the DC internal resistance of each battery;

[0092] A second fitting module 404, configured to perform fitting based on the total number of cycles N of all the batteries and the corresponding change slope R of the DC internal resistance, so as to obtain a prediction function for predicting the lifespan of the lithium iron phosphate battery;

[0093] A prediction module 405, configured to determine the cycle life of the lithium iron phosphate battery to be predicted based on the prediction function.

[0094] In another embodiment of the present invention, a storage medium for predicting the lifespan of a lithium iron phosphate battery is further provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method for predicting the lifespan of a lithium iron phosphate battery are implemented.

[0095] It should be understood that for the foregoing method embodiments, although each step in the flowchart is shown in sequence according to the indication of the arrow, these steps do not necessarily need to be executed in the order indicated by the arrow. Unless there is a clear indication in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, at least some of the steps in the flowchart of the method embodiment may include multiple sub-steps or multiple stages. These sub-steps or stages do not necessarily need to be executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages does not necessarily need to be sequential, but can be executed alternately or alternately with at least a part of other steps or sub-steps or stages of other steps.

[0096] Those of ordinary skill in the art can understand that all or part of the processes of implementing the methods in the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, data processing logics based on quantum computing, etc., without limitation.

[0097] The preferred embodiments of the present invention have been described in detail above. However, the present invention is not limited thereto. Within the scope of the technical concept of the present invention, various simple modifications can be made to the technical solutions of the present invention, including any other suitable combination of each technical feature. These simple modifications and combinations should also be regarded as the content disclosed by the present invention and fall within the protection scope of the present invention.

Claims

1. A method for predicting the life of a lithium iron phosphate battery, characterized in that: The method comprises: Obtaining cycle curves of multiple batteries and the total number of cycles N corresponding thereto, wherein the cycle curve is a curve when the battery is cycled to a preset value of the rated capacity at a preset temperature and a preset voltage, and the preset temperatures of the batteries are the same, and the preset pressures thereof are different; Based on the cycle curve of each battery, a DC internal resistance corresponding to the battery is obtained according to a preset number of cycles; Performing linear fitting based on the preset cycle number of each battery and the DC internal resistance corresponding thereto, respectively, to obtain a DC internal resistance change slope R of each battery; Perform linear fitting based on the total number of cycles N of all the batteries and the corresponding DC internal resistance change slope R to obtain a prediction function for predicting the life of the lithium iron phosphate battery; Based on the prediction function, the cycle life of the lithium iron phosphate battery to be predicted is determined.

2. The method according to claim 1, wherein: The cycle curves of the multiple batteries include a curve when at least one of the batteries is cycled at a preset voltage less than 1V to a capacity of a preset value of the rated capacity, and a curve when at least one of the batteries is cycled at a preset voltage not less than 1V to a capacity of a preset value of the rated capacity.

3. The method according to claim 1 or 2, wherein: The preset temperature is 20-65°C, preferably 40-45°C.

4. The method according to claim 3, wherein: The preset number of cycles does not exceed 300.

5. The method according to claim 4, wherein: Determining that the preset voltage is less than 1V, and the preset number of cycles includes cycling to at least one of the 50th-100th cycles; It is determined that the preset voltage is not less than 1V, and the preset number of cycles is not less than 100.

6. The method according to any one of claims 1 to 5, wherein: The step of determining the cycle life of the lithium iron phosphate battery to be predicted based on the prediction function comprises: Obtain the DC internal resistance change slope R of the lithium iron phosphate battery to be predicted at the test voltage and preset temperature 待测 ; The R 待测 Substitute into the prediction function to obtain the cycle life of the lithium iron phosphate battery to be predicted.

7. The method according to claim 5, wherein: The number of the preset cycle numbers is no less than 3, and they are an arithmetic progression.

8. The method according to claim 7, wherein: Determine that the preset voltage is less than 1V, and the number of the preset cycles is 3; and / or, It is determined that the preset voltage is not less than 1V, and the number of the preset cycles is 4.

9. A device for predicting the life of a lithium iron phosphate battery, characterized in that: The device comprises: A first acquisition module is used to acquire cycle curves of multiple batteries and a total number of cycles N corresponding thereto, wherein the cycle curve is a curve when the battery is cycled to a preset value of a rated capacity at a preset temperature and a preset voltage, and the preset temperatures of the batteries are the same, and the preset pressures thereof are different; A second acquisition module, configured to acquire a DC internal resistance corresponding to each battery according to a preset number of cycles based on the cycle curve of each battery; A first fitting module, configured to perform linear fitting based on the preset cycle number of each battery and the DC internal resistance corresponding thereto, to obtain a DC internal resistance change slope R of each battery; A second fitting module is used to perform fitting based on the total number of cycles N of all the batteries and the corresponding DC internal resistance change slope R to obtain a prediction function for predicting the life of the lithium iron phosphate battery; The prediction module is used to determine the cycle life of the lithium iron phosphate battery to be predicted based on the prediction function.

10. A storage medium for predicting the life of a lithium iron phosphate battery, having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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